Emission Pathway for 6W/m2 Toshihiko Masui, Kenichi Matsumoto, Yasuaki Hijioka, Tsuguki Kinoshita, Toru Nozawa, Sawako Ishiwatari, Mikiko Kainuma (National Institute for Environmental Studies) Etsushi Kato (Japan Agency for Marine ‐ Earth Science and Technology) IAMC Meeting Tsukuba, Japan September 15, 2009
Flowchart of RCP6.0 Flowchart of RCP6.0 Radiative forcing AIM/Impact [Policy] Population/GD P scenario GHG/Aerosol Global emission path National Population/GDP Region Downscaling model Grid cell Base year AIM/CGE [Global] data Pop./GDP Grid cell data Landuse Land ‐ use Emission scenario downscaling change [region] model Emission Land ‐ cover/ Ecosystem downscaling model land ‐ use model Emission [fire, land ‐ use change] Emission [others]
structure of AIM/CGE structure of AIM/CGE GHGs emissions Production sectors production factor market produced commodity capital market land climate labor food resource energy change service GHGs ... emissions Final demand sector trade Japan China ... Annual parameter change Energy technology model: energy efficiency Agriculture model: land productivity ... feedback eg. land productivity change due to climate change scenarios: population, GDP, ...
Results of AIM/CGE (Reference) Results of AIM/CGE (Reference) 250 12000 XAF XLM GDP Population XRE XE10 10000 200 XE15 XRA trillion US$2000 zaf xme 8000 150 rus bra miillion 6000 arg mex 100 usa can 4000 xsa ind 50 xse tha 2000 idn kor 0 0 jpn chn 2000 2020 2040 2060 2080 2100 2000 2020 2040 2060 2080 2100 nzl aus 30000 XAF XLM CO2 XRE XE10 25000 XE15 XRA zaf xme 20000 rus bra arg mex TgC 15000 usa can xsa ind 10000 xse tha idn kor 5000 jpn chn nzl aus 0 2000 2020 2040 2060 2080 2100
Results of AIM/CGE (6W/m 2 2 ) ) Results of AIM/CGE (6W/m 250 12000 XAF XLM GDP Population XRE XE10 10000 200 XE15 XRA trillion US$2000 zaf xme 8000 150 rus bra miillion 6000 arg mex 100 usa can 4000 xsa ind 50 xse tha 2000 idn kor 0 0 jpn chn 2000 2020 2040 2060 2080 2100 2000 2020 2040 2060 2080 2100 nzl aus 16000 XAF XLM CO2 XRE XE10 14000 XE15 XRA 12000 zaf xme rus bra 10000 arg mex TgC 8000 usa can 6000 xsa ind xse tha 4000 idn kor 2000 jpn chn nzl aus 0 2000 2020 2040 2060 2080 2100
Results of AIM/CGE (Reference) Results of AIM/CGE (Reference) 1200 Wind, Solar, 1200 XAF XLM Primary energy Primary energy Geoth., othe XRE XE10 1000 hydro 1000 XE15 XRA 800 zaf xme nuclear 800 rus bra 600 EJ coal 600 EJ arg mex usa can 400 400 biomass xsa ind xse tha 200 200 gas idn kor 0 0 jpn chn oil 2000 2020 2040 2060 2080 2100 nzl aus 2000 2020 2040 2060 2080 2100 1000 1000 Final energy XAF XLM Final energy Electricity 900 900 XRE XE10 800 800 XE15 XRA 700 700 Liquids zaf xme 600 600 rus bra 500 EJ 500 EJ arg mex Solids 400 400 usa can 300 300 xsa ind 200 other 200 xse tha 100 100 idn kor 0 0 jpn chn gas 2000 2020 2040 2060 2080 2100 nzl aus 2000 2020 2040 2060 2080 2100
Results of AIM/CGE (6W/m 2 2 ) ) Results of AIM/CGE (6W/m 900 900 Wind, Solar, XAF XLM Primary energy Primary energy Geoth., other 800 800 XRE XE10 hydro XE15 XRA 700 700 zaf xme 600 600 nuclear rus bra 500 500 EJ EJ arg mex coal 400 400 usa can 300 300 biomass xsa ind 200 200 xse tha gas 100 100 idn kor 0 0 jpn chn oil 2000 2020 2040 2060 2080 2100 2000 2020 2040 2060 2080 2100 nzl aus 800 800 XAF XLM Final energy Electricity Final energy 700 XRE XE10 700 XE15 XRA 600 600 Liquids zaf xme 500 500 rus bra 400 EJ 400 EJ arg mex Solids 300 usa can 300 xsa ind 200 200 other xse tha 100 100 idn kor 0 0 jpn chn gas 2000 2020 2040 2060 2080 2100 nzl aus 2000 2020 2040 2060 2080 2100
Results of AIM/CGE (Reference) Results of AIM/CGE (Reference) 120 120 XAF XLM SAV SO2 SO2 XRE XE10 100 100 AWB XE15 XRA RES zaf xme 80 80 rus bra TgSO2 IND TgSO2 60 arg mex 60 WST usa can 40 40 ENE xsa ind xse tha 20 ISH 20 idn kor TRA 0 jpn chn 0 LUC nzl aus 2000 2020 2040 2060 2080 2100 2000 2020 2040 2060 2080 2100 400 400 XAF XLM SAV CH4 CH4 XRE XE10 350 350 AGR XE15 XRA 300 300 AWB zaf xme 250 250 RES rus bra TgCH4 TgCH4 200 IND 200 arg mex usa can 150 WST 150 xsa ind ENE 100 100 xse tha ISH 50 50 idn kor TRA 0 0 jpn chn LUC nzl aus 2000 2020 2040 2060 2080 2100 2000 2020 2040 2060 2080 2100
Results of AIM/CGE (6W/m 2 2 ) ) Results of AIM/CGE (6W/m 120 120 XAF XLM SAV SO2 SO2 XRE XE10 100 AWB 100 XE15 XRA RES zaf xme 80 80 rus bra IND TgSO2 TgSO2 60 60 arg mex WST usa can 40 40 ENE xsa ind xse tha ISH 20 20 idn kor TRA 0 0 jpn chn LUC 2000 2020 2040 2060 2080 2100 2000 2020 2040 2060 2080 2100 nzl aus 400 400 XAF XLM SAV CH4 CH4 XRE XE10 350 350 AGR XE15 XRA 300 300 AWB zaf xme 250 250 RES rus bra TgCH4 TgCH4 200 IND 200 arg mex usa can 150 WST 150 xsa ind ENE 100 100 xse tha ISH 50 50 idn kor TRA 0 0 jpn chn LUC nzl aus 2000 2020 2040 2060 2080 2100 2000 2020 2040 2060 2080 2100
Spatial explicit population/GDP scenario Spatial explicit population/GDP scenario Data Population Data Population Data GDP Data GDP 224 countries 183 countries UN population UN population IFs GDP scenario Population data GDP data IFs GDP scenario （UN shot range) （UN shot range) (2000-2050) (2000-2100) IIASA Population scenario 224 countries 224 countries UN population UN population Population data GDP data GTAP GDP in 2001 GTAP GDP in 2001 （UN Long range） （UN Long range） (2000-2100) (2000-2100) Urban area Population rank ‐ size rank ‐ size rule rule ３０ second 30 second Income gap Grid cell pop. Grid cell GDP UN Urbanization Income gap UN Urbanization (2000-2100) (2000-2100)
Population scenario Population scenario 2000 2050 2100
Landuse downscaling model Landuse downscaling model Geophysical constraint ・ Built ‐ up area < 5 degree ・ Forest < 20 degree 0.5 degree etc. 1. Urban (GDP, crop price…) 2. Cropland (yield, slope angle…) 3. Pasture (NPP, slope angle…) 4. Harvest forest (population density..) 1km
Results (Land- -use scenario) use scenario) Results (Land Cropland Cropland 2000 2050 2000 2050 2100 2100
Results (Land- -use scenario) use scenario) Results (Land Pasture Pasture 2000 2050 2000 2050 4.00E+07 3.50E+07 3.00E+07 ] 2 2.50E+07 m k [ e 2.00E+07 r u t s 1.50E+07 a P 1.00E+07 5.00E+06 0.00E+00 2000 2005 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2100 2100 XAF XLM XRE XE0 XE5 XRA zaf xme rus bra arg mex usa can xsa ind xse tha idn kor jpn chn nzl aus
Emission downscaling model Emission downscaling model AIM 24 region Sector region indicator for downscale electricity Japan population electricity China population … … … agriculture USA agricultural area … … … From IAM Summed up ・ Power plant & energy conv. （ by population ） ・ Industry: process & combustion （ by GDP ） ・ Solvent use （ by GDP ） Downscale by ・ Residential & commercial （ by rural pop ） indicator ・ Waste （ by population ） ・ Agriculture: waste （ by agriculture ） Global ・ International shipping distribution ・ Aviation ・ Transportation (road & railroad) Regional ・ Agriculture : Animal & Soil distribution
Case 1 Case 1 Changes in regional emissions are downscaled according to spatially explicit indicators for each sector and each region. ENE （ total population ） , IND （ GDP ） , SLV （ GDP ） , DOM （ rural population ） , WST （ total population ） & AWB （ cropland area ） w ( x , y , t ) r , s E ( x , y , t ) E ( x , y , t t ) e ( t ) e ( t t ) s s r s r s , , w ( x , y , t ) dxdy r r , s y ： sector ： longitude ： latitude ： year ： region s x t r ： gridded emissions from a sector s E s ( x , y , t ) ： regional emissions for region and for sector estimated by IAM r s e , t ( ) r s ： spatially explicit indicator for region and for sector r s w s ( x , y , t ) r ,
Spatial explicit emission scenarios Spatial explicit emission scenarios Case 1 (Industry, NO2) 2000 2050 2000 2050 2100 2100
Case 2 Case 2 Global distribution at year 2000 is scaled by world total emissions. SHP & AIR e ( t ) E ( x , y , t ) E ( x , y , t ) s s s e ( t ) 0 s 0 y ： sector ： longitude ： latitude ： year s x t s ： gridded emissions from a sector E s ( x , y , t ) ： global emissions for sector estimated by IAM s e s ( t )
Spatial explicit emission scenarios Spatial explicit emission scenarios Case 2 (International shipping, SO2) 2000 2050 2000 2050 2100 2100
Recommend
More recommend
